增加行为树对比和测试
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Caiyishuai
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@ -15,24 +15,17 @@ class Bahavior(ptree.behaviour.Behaviour):
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scene = None
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print_name_prefix = ""
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tables_for_placement = {'Bar', 'Bar2', 'WaterTable', 'CoffeeTable', 'Table1', 'Table2', 'Table3',"BrightTable6"}
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# all_object = {'Coffee', 'Water', 'Dessert', 'Softdrink', 'BottledDrink', 'Yogurt', 'ADMilk', 'MilkDrink', 'Milk',
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# 'VacuumCup'}
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all_object = {
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'Coffee', 'Water', 'Dessert', 'Softdrink', 'BottledDrink', 'Yogurt', 'ADMilk', 'MilkDrink', 'Milk','VacuumCup',
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'Chips', 'NFCJuice', 'Bernachon', 'ADMilk', 'SpringWater'}
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# BrightTable5 = Table4
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tables_for_guiding = {"QuietTable1","QuietTable2",
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"BrightTable1","BrightTable2","BrightTable3","BrightTable4","BrightTable5","BrightTable6"
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'CoffeeTable','WaterTable','Table1', 'Table2', 'Table3'}
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# all_place = {'Bar', 'WaterTable', 'CoffeeTable'}
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# all_object = {'Coffee', 'Water', 'Dessert', 'Softdrink', 'Yogurt'}
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# all_object = {'Coffee', 'Water'}
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# all_object = set()
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# all_place=set()
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# tables_for_placement = {'Bar', 'WaterTable', 'CoffeeTable'}
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# all_object = {'Coffee', 'Water', 'Dessert', 'Softdrink', 'Yogurt'}
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num_of_obj_on_place={
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'Bar': 0, # (247.0, 520.0, 100.0)
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@ -15,7 +15,8 @@ class FreeHands(Act):
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@classmethod
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def get_info(cls):
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info = {}
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info["pre"]= set()
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# info["pre"]= set()
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info["pre"] ={f'Holding(Nothing)'}
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info['add'] = {f'Holding(Nothing)'}
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info['del_set'] = {f'Holding({obj})' for obj in cls.all_object}
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info['cost'] = 0
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@ -0,0 +1,297 @@
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import random
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import numpy as np
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import copy
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import time
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from robowaiter.behavior_tree.obtea.BehaviorTree import Leaf,ControlBT
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from robowaiter.behavior_tree.obtea.OptimalBTExpansionAlgorithm import Action,generate_random_state,state_transition,conflict
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# 本文所提出的完备规划算法
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class BTalgorithm:
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def __init__(self,verbose=False):
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self.bt = None
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self.nodes = []
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self.traversed = []
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self.conditions = []
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self.conditions_index = []
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self.verbose = verbose
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# print (self.conditions_list[0])
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def clear(self):
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self.bt = None
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self.nodes = []
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self.traversed = []
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self.conditions = []
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self.conditions_index = []
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# 运行规划算法，从初始状态、目标状态和可用行动，计算行为树self.bt
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def run_algorithm(self, start, goal, actions):
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# 初始行为树只包含目标条件
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self.bt = ControlBT(type='cond')
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g_node = Leaf(type='cond', content=goal,mincost=0)
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self.bt.add_child([g_node])
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self.conditions.append(goal)
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self.nodes.append(g_node) # condition node list
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# 尝试在初始状态执行行为树
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val, obj = self.bt.tick(start)
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canrun = False
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if val == 'success' or val == 'running':
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canrun = True
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# 循环扩展，直到行为树能够在初始状态运行
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while not canrun:
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index = -1
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for i in range(0, len(self.nodes)):
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if self.nodes[i].content in self.traversed:
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continue
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else:
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c_node = self.nodes[i]
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index = i
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break
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if index == -1: # 树中结点扩展完毕，仍无法运行行为树，返回失败
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print('Failure')
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return False
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# 根据所选择条件结点扩展子树
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subtree = ControlBT(type='?')
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subtree.add_child([copy.deepcopy(c_node)]) # 子树首先保留所扩展结点
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c = c_node.content # 子树所扩展结点对应的条件（一个文字的set）
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for i in range(0, len(actions)): # 选择符合条件的行动，
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# print("have action")
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if not c & ((actions[i].pre | actions[i].add) - actions[i].del_set) <= set():
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# print ("pass add")
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if (c - actions[i].del_set) == c:
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# print("pass delete")
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c_attr = (actions[i].pre | c) - actions[i].add
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valid = True
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# 这样剪枝存在错误性
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if conflict(c_attr):
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continue
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for j in self.traversed: # 剪枝操作
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if j <= c_attr:
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valid = False
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break
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if valid:
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# print("pass prune")
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# 构建行动的顺序结构
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sequence_structure = ControlBT(type='>')
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c_attr_node = Leaf(type='cond', content=c_attr, mincost=0)
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a_node = Leaf(type='act', content=actions[i], mincost=0)
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sequence_structure.add_child([c_attr_node, a_node])
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# 将顺序结构添加到子树
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subtree.add_child([sequence_structure])
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self.nodes.append(c_attr_node)
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# 将原条件结点c_node替换为扩展后子树subtree
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parent_of_c = c_node.parent
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parent_of_c.children[0] = subtree
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# 记录已扩展条件
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self.traversed.append(c)
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# 尝试在初始状态运行行为树
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val, obj = self.bt.tick(start)
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canrun = False
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if val == 'success' or val == 'running':
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canrun = True
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return True
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def print_solution(self):
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print(len(self.nodes))
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# for i in self.nodes:
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# if isinstance(i,Node):
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# print (i.content)
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# else:
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# print (i)
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# 树的dfs
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def dfs_ptml(self,parnode,is_root=False):
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for child in parnode.children:
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if isinstance(child, Leaf):
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if child.type == 'cond':
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if is_root and len(child.content) > 1:
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# 把多个 cond 串起来
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self.ptml_string += "sequence{\n"
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self.ptml_string += "cond "
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c_set_str = '\n cond '.join(map(str, child.content)) + "\n"
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self.ptml_string += c_set_str
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self.ptml_string += '}\n'
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else:
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self.ptml_string += "cond "
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c_set_str = '\n cond '.join(map(str, child.content)) + "\n"
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self.ptml_string += c_set_str
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elif child.type == 'act':
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if '(' not in child.content.name:
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self.ptml_string += 'act ' + child.content.name + "()\n"
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else:
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self.ptml_string += 'act ' + child.content.name + "\n"
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elif isinstance(child, ControlBT):
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if child.type == '?':
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self.ptml_string += "selector{\n"
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self.dfs_ptml(parnode=child)
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elif child.type == '>':
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self.ptml_string += "sequence{\n"
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self.dfs_ptml( parnode=child)
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self.ptml_string += '}\n'
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def get_ptml(self):
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self.ptml_string = "selector{\n"
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self.dfs_ptml(self.bt.children[0],is_root=True)
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self.ptml_string += '}\n'
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return self.ptml_string
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def save_ptml_file(self,file_name):
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self.ptml_string = "selector{\n"
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self.dfs_ptml(self.bt.children[0])
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self.ptml_string += '}\n'
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with open(f'./{file_name}.ptml', 'w') as file:
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file.write(self.ptml_string)
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return self.ptml_string
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# 所对比的基准算法，具体扩展细节有差异
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if __name__ == '__main__':
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random.seed(1)
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# 设置生成规划问题集的超参数：文字数、解深度、迭代次数
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literals_num = 10
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depth = 10
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iters = 10
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total_tree_size = []
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total_action_num = []
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total_state_num = []
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total_steps_num = []
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# fail_count=0
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# danger_count=0
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success_count = 0
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failure_count = 0
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planning_time_total = 0.0
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# 实验1000次
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for count in range(0, 1000):
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# 生成一个规划问题，包括随机的状态和行动，以及目标状态
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states = []
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actions = []
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start = generate_random_state(literals_num)
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state = start
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states.append(state)
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# print (state)
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for i in range(0, depth):
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a = Action()
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a.generate_from_state(state, literals_num)
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if not a in actions:
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actions.append(a)
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state = state_transition(state, a)
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if state in states:
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pass
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else:
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states.append(state)
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# print(state)
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goal = states[-1]
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state = start
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for i in range(0, iters):
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a = Action()
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a.generate_from_state(state, literals_num)
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if not a in actions:
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actions.append(a)
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state = state_transition(state, a)
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if state in states:
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pass
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else:
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states.append(state)
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state = random.sample(states, 1)[0]
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# 选择测试本文算法btalgorithm，或对比算法weakalgorithm
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algo = BTalgorithm()
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# algo = Weakalgorithm()
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start_time = time.time()
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if algo.run_algorithm(start, goal, list(actions)): # 运行算法，规划后行为树为algo.bt
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total_tree_size.append(algo.bt.count_size() - 1)
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else:
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print("error")
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end_time = time.time()
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planning_time_total += (end_time - start_time)
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# 开始从初始状态运行行为树，测试
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state = start
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steps = 0
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val, obj = algo.bt.tick(state) # tick行为树，obj为所运行的行动
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while val != 'success' and val != 'failure': # 运行直到行为树成功或失败
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state = state_transition(state, obj)
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val, obj = algo.bt.tick(state)
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if (val == 'failure'):
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print("bt fails at step", steps)
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steps += 1
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if (steps >= 500): # 至多运行500步
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break
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if not goal <= state: # 错误解，目标条件不在执行后状态满足
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# print ("wrong solution",steps)
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failure_count += 1
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else: # 正确解，满足目标条件
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# print ("right solution",steps)
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success_count += 1
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total_steps_num.append(steps)
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algo.clear()
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total_action_num.append(len(actions))
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total_state_num.append(len(states))
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print(success_count, failure_count) # 算法成功和失败次数
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print(np.mean(total_tree_size), np.std(total_tree_size, ddof=1)) # 1000次测试树大小
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print(np.mean(total_steps_num), np.std(total_steps_num, ddof=1))
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print(np.mean(total_state_num)) # 1000次问题的平均状态数
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print(np.mean(total_action_num)) # 1000次问题的平均行动数
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print(planning_time_total, planning_time_total / 1000.0)
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# print(total_state_num)
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# casestudy begin 对应论文的case study，包含三个行动的移动机械臂场景
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actions = []
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a = Action(name='movebtob')
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a.pre = {1, 2}
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a.add = {3}
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a.del_set = {1, 4}
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actions.append(a)
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a = Action(name='moveatob')
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a.pre = {1}
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a.add = {5, 2}
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a.del_set = {1, 6}
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actions.append(a)
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a = Action(name='moveatoa')
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a.pre = {7}
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a.add = {8, 2}
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a.del_set = {7, 6}
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actions.append(a)
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start = {1, 7, 4, 6}
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goal = {3}
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algo = BTalgorithm()
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algo.clear()
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algo.run_algorithm(start, goal, list(actions))
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state = start
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steps = 0
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val, obj = algo.bt.tick(state)
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while val != 'success' and val != 'failure':
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state = state_transition(state, obj)
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print(obj.name)
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val, obj = algo.bt.tick(state)
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if (val == 'failure'):
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print("bt fails at step", steps)
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steps += 1
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if not goal <= state:
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print("wrong solution", steps)
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else:
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print("right solution", steps)
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# algo.bt.print_nodes()
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print(algo.bt.count_size() - 1)
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algo.clear()
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# case study end
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@ -20,6 +20,24 @@ class Action:
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def __str__(self):
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return self.name
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# 从状态随机生成一个行动
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def generate_from_state(self,state,num):
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for i in range(0,num):
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if i in state:
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if random.random() >0.5:
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self.pre.add(i)
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if random.random() >0.5:
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self.del_set.add(i)
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continue
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if random.random() > 0.5:
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self.add.add(i)
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continue
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if random.random() >0.5:
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self.del_set.add(i)
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def print_action(self):
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print (self.pre)
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print(self.add)
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print(self.del_set)
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#生成随机状态
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def generate_random_state(num):
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@ -68,9 +86,9 @@ class OptBTExpAlgorithm:
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self.conditions_index = []
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#运行规划算法，从初始状态、目标状态和可用行动，计算行为树self.bt
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def run_algorithm(self,goal,actions,scene):
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self.scene = scene
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# def run_algorithm(self,goal,actions,scene):
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def run_algorithm(self, start, goal, actions):
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# self.scene = scene
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if self.verbose:
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print("\n算法开始！")
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@ -113,7 +131,6 @@ class OptBTExpAlgorithm:
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min_cost = cond_anc_pair.cond_leaf.mincost
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pair_node = copy.deepcopy(cond_anc_pair)
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index = i
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break
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if self.verbose:
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print("选择扩展条件结点：",pair_node.cond_leaf.content)
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@ -141,7 +158,8 @@ class OptBTExpAlgorithm:
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subtree.add_child([copy.deepcopy(sequence_structure)]) # subtree 是回不断变化的，它的父亲是self.bt
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self.expanded.append(copy.deepcopy(pair_node))
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# 增加实时条件判断，满足条件就不再扩展
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if c <= self.scene.state["condition_set"]:
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# if c <= self.scene.state["condition_set"]:
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if c <= start:
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return True
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else:
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subtree.add_child([copy.deepcopy(pair_node.act_leaf)])
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@ -158,6 +176,11 @@ class OptBTExpAlgorithm:
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current_mincost = pair_node.cond_leaf.mincost # 当前的最短路径是多少
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for i in range(0, len(actions)):
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if actions[i].name == 'FreeHands()':
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kk=1
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if not c & ((actions[i].pre | actions[i].add) - actions[i].del_set) <= set():
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if (c - actions[i].del_set) == c:
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if self.verbose:
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@ -1,5 +1,6 @@
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from robowaiter.behavior_tree.obtea.OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm,state_transition # 调用最优行为树扩展算法
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from robowaiter.behavior_tree.obtea.BTExpansionAlgorithm import BTalgorithm # 调用最优行为树扩展算法
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from robowaiter.behavior_tree.obtea.examples import *
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@ -23,6 +24,8 @@ class BTOptExpInterface:
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self.has_processed = False
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self.scene = scene
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self.bt_algo_opt = self.scene.bt_algo_opt
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def process(self, goal):
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"""
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@ -31,9 +34,15 @@ class BTOptExpInterface:
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:return: A PTML string representing the outcome of the behavior tree.
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"""
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self.goal = goal
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if self.bt_algo_opt:
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self.algo = OptBTExpAlgorithm(verbose=False)
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else:
|
||||
self.algo = BTalgorithm(verbose=False)
|
||||
|
||||
self.algo.clear()
|
||||
self.algo.run_algorithm(self.goal, self.actions,self.scene) # 调用算法得到行为树保存至 algo.bt
|
||||
self.algo.run_algorithm(self.scene.state["condition_set"],self.goal, self.actions) # 调用算法得到行为树保存至 algo.bt
|
||||
|
||||
|
||||
self.ptml_string = self.algo.get_ptml()
|
||||
self.has_processed = True
|
||||
# algo.print_solution() # print behavior tree
|
||||
|
|
|
|||
|
|
@ -222,12 +222,6 @@ get_object_info
|
|||
好的，我明白了，那么我们推荐您到大厅的桌子，那里的空间比较宽敞，环境也比较明亮，适合带着孩子一起用餐。
|
||||
|
||||
|
||||
冰红茶
|
||||
好的
|
||||
create_sub_task
|
||||
{"goal":"On(Yogurt,WaterTable)"}
|
||||
|
||||
|
||||
水杯
|
||||
好的
|
||||
create_sub_task
|
||||
|
|
@ -259,3 +253,8 @@ create_sub_task
|
|||
好的
|
||||
create_sub_task
|
||||
{"goal":"On(Dessert,Bar)"}
|
||||
|
||||
打开空调并降低空调温度
|
||||
好的，没问题
|
||||
create_sub_task
|
||||
{"goal":"Is(ACTemperature,Down)"}
|
||||
|
|
@ -19,7 +19,7 @@ root_path = get_root_path()
|
|||
ptml_path = os.path.join(root_path, 'robowaiter/robot/Default.ptml')
|
||||
behavior_lib_path = os.path.join(root_path, 'robowaiter/behavior_lib')
|
||||
|
||||
|
||||
from robowaiter.utils.bt.draw import render_dot_tree
|
||||
|
||||
class Robot(object):
|
||||
scene = None
|
||||
|
|
@ -79,6 +79,7 @@ class Robot(object):
|
|||
file.write(ptml_string)
|
||||
|
||||
sub_task_bt = load_bt_from_ptml(self.scene, file_path,self.behavior_lib_path)
|
||||
render_dot_tree(sub_task_bt.root, target_directory=self.scene.output_path, name="expanded_bt", png_only=False)
|
||||
|
||||
# 加入删除子树的节点
|
||||
seq = Sequence(name="Sequence", memory=False)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,37 @@
|
|||
digraph pastafarianism {
|
||||
ordering=out;
|
||||
graph [fontname="times-roman"];
|
||||
node [fontname="times-roman"];
|
||||
edge [fontname="times-roman"];
|
||||
"87355232-8ad6-484e-b363-38536e737846" [fillcolor=cyan, fontcolor=black, fontsize=20, height=0.01, label=Selector, shape=diamond, style=filled, width=0.01];
|
||||
"3f017683-7aa3-40d3-b2be-cc32784313a1" [fillcolor=yellow, fontcolor=black, fontsize=20, label="Is(ACTemperature,Down)", shape=ellipse, style=filled];
|
||||
"87355232-8ad6-484e-b363-38536e737846" -> "3f017683-7aa3-40d3-b2be-cc32784313a1";
|
||||
"b900e9cb-c0a8-4e8d-8a9b-d871012abefb" [fillcolor=orange, fontcolor=black, fontsize=20, height=0.01, label=Sequence, shape=octagon, style=filled, width=0.01];
|
||||
"87355232-8ad6-484e-b363-38536e737846" -> "b900e9cb-c0a8-4e8d-8a9b-d871012abefb";
|
||||
"10ef3ff4-7acb-4991-9e63-6e489c3ef22d" [fillcolor=yellow, fontcolor=black, fontsize=20, label="Holding(Nothing)", shape=ellipse, style=filled];
|
||||
"b900e9cb-c0a8-4e8d-8a9b-d871012abefb" -> "10ef3ff4-7acb-4991-9e63-6e489c3ef22d";
|
||||
"a3869371-404d-4085-932b-232e188fb133" [fillcolor=yellow, fontcolor=black, fontsize=20, label="Is(AC,On)", shape=ellipse, style=filled];
|
||||
"b900e9cb-c0a8-4e8d-8a9b-d871012abefb" -> "a3869371-404d-4085-932b-232e188fb133";
|
||||
"3a5cbe8f-7672-4a34-91d1-87502aed457c" [fillcolor=lawngreen, fontcolor=black, fontsize=20, label="Turn(ACTemperature,Down)", shape=box, style=filled];
|
||||
"b900e9cb-c0a8-4e8d-8a9b-d871012abefb" -> "3a5cbe8f-7672-4a34-91d1-87502aed457c";
|
||||
"b64db1a4-d79f-47b4-99db-f184b38d1361" [fillcolor=orange, fontcolor=black, fontsize=20, height=0.01, label=Sequence, shape=octagon, style=filled, width=0.01];
|
||||
"87355232-8ad6-484e-b363-38536e737846" -> "b64db1a4-d79f-47b4-99db-f184b38d1361";
|
||||
"c6eca6dd-9fca-4bd3-b054-9b43fe02613a" [fillcolor=yellow, fontcolor=black, fontsize=20, label="Is(AC,On)", shape=ellipse, style=filled];
|
||||
"b64db1a4-d79f-47b4-99db-f184b38d1361" -> "c6eca6dd-9fca-4bd3-b054-9b43fe02613a";
|
||||
"df04f6f4-d471-4519-ab9e-5d5bd8dfbd4c" [fillcolor=lawngreen, fontcolor=black, fontsize=20, label="FreeHands()", shape=box, style=filled];
|
||||
"b64db1a4-d79f-47b4-99db-f184b38d1361" -> "df04f6f4-d471-4519-ab9e-5d5bd8dfbd4c";
|
||||
"8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e" [fillcolor=orange, fontcolor=black, fontsize=20, height=0.01, label=Sequence, shape=octagon, style=filled, width=0.01];
|
||||
"87355232-8ad6-484e-b363-38536e737846" -> "8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e";
|
||||
"82a23405-c3c6-47dc-bf7b-0636e3efe52c" [fillcolor=yellow, fontcolor=black, fontsize=20, label="Holding(Nothing)", shape=ellipse, style=filled];
|
||||
"8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e" -> "82a23405-c3c6-47dc-bf7b-0636e3efe52c";
|
||||
"010d42ee-18b3-4a0e-9be1-eab67afaca60" [fillcolor=yellow, fontcolor=black, fontsize=20, label="Is(AC,Off)", shape=ellipse, style=filled];
|
||||
"8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e" -> "010d42ee-18b3-4a0e-9be1-eab67afaca60";
|
||||
"90cdf2ff-98b0-4454-803b-feee5f5475bf" [fillcolor=lawngreen, fontcolor=black, fontsize=20, label="Turn(AC,On)", shape=box, style=filled];
|
||||
"8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e" -> "90cdf2ff-98b0-4454-803b-feee5f5475bf";
|
||||
"e65ce322-edaa-4677-ab68-4795e57648da" [fillcolor=orange, fontcolor=black, fontsize=20, height=0.01, label=Sequence, shape=octagon, style=filled, width=0.01];
|
||||
"87355232-8ad6-484e-b363-38536e737846" -> "e65ce322-edaa-4677-ab68-4795e57648da";
|
||||
"7d0c1403-c5d9-4bcd-a0db-f3e527a3be93" [fillcolor=yellow, fontcolor=black, fontsize=20, label="Is(AC,Off)", shape=ellipse, style=filled];
|
||||
"e65ce322-edaa-4677-ab68-4795e57648da" -> "7d0c1403-c5d9-4bcd-a0db-f3e527a3be93";
|
||||
"931d4ea6-c661-4ea8-9016-2d4e17d30b19" [fillcolor=lawngreen, fontcolor=black, fontsize=20, label="FreeHands()", shape=box, style=filled];
|
||||
"e65ce322-edaa-4677-ab68-4795e57648da" -> "931d4ea6-c661-4ea8-9016-2d4e17d30b19";
|
||||
}
|
||||
Binary file not shown.
|
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|
|
@ -0,0 +1,199 @@
|
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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
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<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
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<!-- Generated by graphviz version 9.0.0 (20230911.1827)
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-->
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<!-- Title: pastafarianism Pages: 1 -->
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<svg width="1818pt" height="235pt"
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<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 230.53)">
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<title>pastafarianism</title>
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<polygon fill="black" stroke="black" points="780.67,-49.81 771.31,-44.85 775.53,-54.56 780.67,-49.81"/>
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</g>
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<!-- df04f6f4-d471-4519-ab9e-5d5bd8dfbd4c -->
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<g id="node9" class="node">
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<title>df04f6f4-d471-4519-ab9e-5d5bd8dfbd4c</title>
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<polygon fill="lawngreen" stroke="black" points="957.82,-40.1 840.57,-40.1 840.57,-4.1 957.82,-4.1 957.82,-40.1"/>
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<text text-anchor="middle" x="899.2" y="-14.72" font-family="Times New Roman,serif" font-size="20.00">FreeHands()</text>
|
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</g>
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<!-- b64db1a4-d79f-47b4-99db-f184b38d1361->df04f6f4-d471-4519-ab9e-5d5bd8dfbd4c -->
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<g id="edge8" class="edge">
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<title>b64db1a4-d79f-47b4-99db-f184b38d1361->df04f6f4-d471-4519-ab9e-5d5bd8dfbd4c</title>
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<path fill="none" stroke="black" d="M846.6,-79.97C855.58,-70.26 866.06,-58.93 875.29,-48.94"/>
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<polygon fill="black" stroke="black" points="877.77,-51.42 881.99,-41.71 872.63,-46.67 877.77,-51.42"/>
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</g>
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<!-- 82a23405-c3c6-47dc-bf7b-0636e3efe52c -->
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<g id="node11" class="node">
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<title>82a23405-c3c6-47dc-bf7b-0636e3efe52c</title>
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<ellipse fill="yellow" stroke="black" cx="1090.2" cy="-22.1" rx="114.2" ry="22.1"/>
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<text text-anchor="middle" x="1090.2" y="-14.72" font-family="Times New Roman,serif" font-size="20.00">Holding(Nothing)</text>
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</g>
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<!-- 8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e->82a23405-c3c6-47dc-bf7b-0636e3efe52c -->
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<g id="edge10" class="edge">
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<title>8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e->82a23405-c3c6-47dc-bf7b-0636e3efe52c</title>
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<path fill="none" stroke="black" d="M1162.42,-79.75C1150.89,-70.71 1137.61,-60.29 1125.6,-50.87"/>
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<polygon fill="black" stroke="black" points="1127.93,-48.25 1117.9,-44.83 1123.61,-53.76 1127.93,-48.25"/>
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</g>
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<!-- 010d42ee-18b3-4a0e-9be1-eab67afaca60 -->
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<g id="node12" class="node">
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<title>010d42ee-18b3-4a0e-9be1-eab67afaca60</title>
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<ellipse fill="yellow" stroke="black" cx="1294.2" cy="-22.1" rx="72.3" ry="22.1"/>
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<text text-anchor="middle" x="1294.2" y="-14.72" font-family="Times New Roman,serif" font-size="20.00">Is(AC,Off)</text>
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</g>
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<!-- 8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e->010d42ee-18b3-4a0e-9be1-eab67afaca60 -->
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<g id="edge11" class="edge">
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<title>8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e->010d42ee-18b3-4a0e-9be1-eab67afaca60</title>
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<path fill="none" stroke="black" d="M1221.97,-79.75C1233.78,-70.49 1247.44,-59.78 1259.68,-50.18"/>
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<polygon fill="black" stroke="black" points="1261.8,-52.96 1267.51,-44.03 1257.48,-47.45 1261.8,-52.96"/>
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</g>
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<!-- 90cdf2ff-98b0-4454-803b-feee5f5475bf -->
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<g id="node13" class="node">
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<title>90cdf2ff-98b0-4454-803b-feee5f5475bf</title>
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<polygon fill="lawngreen" stroke="black" points="1511.7,-40.1 1384.7,-40.1 1384.7,-4.1 1511.7,-4.1 1511.7,-40.1"/>
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<text text-anchor="middle" x="1448.2" y="-14.72" font-family="Times New Roman,serif" font-size="20.00">Turn(AC,On)</text>
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</g>
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<!-- 8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e->90cdf2ff-98b0-4454-803b-feee5f5475bf -->
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<g id="edge12" class="edge">
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<title>8108bcba-dbeb-4784-8f1f-eb9ede4ffb0e->90cdf2ff-98b0-4454-803b-feee5f5475bf</title>
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<path fill="none" stroke="black" d="M1244,-86.92C1283.65,-74.53 1338.64,-57.34 1381.56,-43.93"/>
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<polygon fill="black" stroke="black" points="1382.53,-47.29 1391.03,-40.96 1380.44,-40.61 1382.53,-47.29"/>
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</g>
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<!-- 7d0c1403-c5d9-4bcd-a0db-f3e527a3be93 -->
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<g id="node15" class="node">
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<title>7d0c1403-c5d9-4bcd-a0db-f3e527a3be93</title>
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<ellipse fill="yellow" stroke="black" cx="1602.2" cy="-22.1" rx="72.3" ry="22.1"/>
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<text text-anchor="middle" x="1602.2" y="-14.72" font-family="Times New Roman,serif" font-size="20.00">Is(AC,Off)</text>
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</g>
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<!-- e65ce322-edaa-4677-ab68-4795e57648da->7d0c1403-c5d9-4bcd-a0db-f3e527a3be93 -->
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<g id="edge14" class="edge">
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<title>e65ce322-edaa-4677-ab68-4795e57648da->7d0c1403-c5d9-4bcd-a0db-f3e527a3be93</title>
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<path fill="none" stroke="black" d="M1602.2,-79.97C1602.2,-72.46 1602.2,-63.99 1602.2,-55.92"/>
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<polygon fill="black" stroke="black" points="1605.7,-56.06 1602.2,-46.06 1598.7,-56.06 1605.7,-56.06"/>
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</g>
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<!-- 931d4ea6-c661-4ea8-9016-2d4e17d30b19 -->
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<g id="node16" class="node">
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<title>931d4ea6-c661-4ea8-9016-2d4e17d30b19</title>
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<polygon fill="lawngreen" stroke="black" points="1809.82,-40.1 1692.57,-40.1 1692.57,-4.1 1809.82,-4.1 1809.82,-40.1"/>
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<text text-anchor="middle" x="1751.2" y="-14.72" font-family="Times New Roman,serif" font-size="20.00">FreeHands()</text>
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</g>
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<!-- e65ce322-edaa-4677-ab68-4795e57648da->931d4ea6-c661-4ea8-9016-2d4e17d30b19 -->
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<g id="edge15" class="edge">
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<title>e65ce322-edaa-4677-ab68-4795e57648da->931d4ea6-c661-4ea8-9016-2d4e17d30b19</title>
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<path fill="none" stroke="black" d="M1640.57,-82.51C1661.56,-71.23 1687.6,-57.25 1709.14,-45.68"/>
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<polygon fill="black" stroke="black" points="1710.51,-48.92 1717.67,-41.1 1707.2,-42.75 1710.51,-48.92"/>
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</g>
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</g>
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</svg>
|
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|
After Width: | Height: | Size: 14 KiB |
|
|
@ -215,12 +215,14 @@ class Scene:
|
|||
"sub_goal_list": [], # 子目标列表
|
||||
"status": None, # 仿真器中的观测信息，见下方详细解释
|
||||
"condition_set": {'At(Robot,Bar)', 'Is(AC,Off)',
|
||||
'Holding(Nothing)', 'Exist(Yogurt)', 'Exist(BottledDrink)',
|
||||
'Exist(Softdrink)',
|
||||
'Exist(Yogurt)', 'Exist(BottledDrink)','Exist(Softdrink)',
|
||||
# 'On(Yogurt,Bar)','On(BottledDrink,Bar)',
|
||||
# 'Exist(Softdrink)', 'On(Softdrink,Table1)',
|
||||
'Exist(Chips)', 'Exist(NFCJuice)', 'Exist(Bernachon)', 'Exist(ADMilk)', 'Exist(SpringWater)',
|
||||
|
||||
# 'Holding(Nothing)',
|
||||
'Holding(Coffee)',
|
||||
|
||||
'Exist(VacuumCup)', 'On(VacuumCup,Table2)',
|
||||
'Is(HallLight,Off)', 'Is(TubeLight,On)', 'Is(Curtain,On)',
|
||||
'Is(Table1,Dirty)', 'Is(Floor,Dirty)', 'Is(Chairs,Dirty)'},
|
||||
|
|
@ -264,7 +266,7 @@ class Scene:
|
|||
# 是否展示UI
|
||||
self.show_ui = False
|
||||
# 图像分割
|
||||
self.take_picture = True
|
||||
self.take_picture = False
|
||||
self.map_ratio = 5
|
||||
self.map_map = np.zeros((math.ceil(950 / self.map_ratio), math.ceil(1850 / self.map_ratio)))
|
||||
self.db = DBSCAN(eps=self.map_ratio, min_samples=int(self.map_ratio / 2))
|
||||
|
|
@ -272,6 +274,8 @@ class Scene:
|
|||
|
||||
self.is_nav_walk = False
|
||||
|
||||
self.bt_algo_opt = True
|
||||
|
||||
file_name = os.path.join(root_path,'robowaiter/algos/navigator/map_5.pkl')
|
||||
if os.path.exists(file_name):
|
||||
with open(file_name, 'rb') as file:
|
||||
|
|
@ -451,6 +455,7 @@ class Scene:
|
|||
print(f'event: {t}, {func.__name__}')
|
||||
self.signal_event_list.pop(0)
|
||||
self.last_event_time = self.time
|
||||
print("==== *args ======:",*args)
|
||||
func(*args)
|
||||
|
||||
def deal_event(self):
|
||||
|
|
@ -474,6 +479,7 @@ class Scene:
|
|||
def set_goal(self, goal):
|
||||
g = eval("{'" + goal + "'}")
|
||||
|
||||
print("====== g =======:",g)
|
||||
def set_sub_task():
|
||||
self.state['chat_list'].append(("Goal", g))
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,48 @@
|
|||
"""
|
||||
人提出请求，机器人完成任务
|
||||
1. 做咖啡（固定动画）：接收到做咖啡指令、走到咖啡机、拿杯子、操作咖啡机、取杯子、送到客人桌子上
|
||||
2. 倒水
|
||||
3. 夹点心
|
||||
|
||||
具体描述：设计一套点单规则（如菜单包含咖啡、水、点心等），按照规则拟造随机的订单。在收到订单后，通过大模型让机器人输出合理的备餐计划，并尝试在模拟环境中按照这个规划实现任务。
|
||||
|
||||
"""
|
||||
import time
|
||||
|
||||
# todo: 接收点单信息，大模型生成任务规划
|
||||
|
||||
from robowaiter.scene.scene import Scene
|
||||
|
||||
class SceneOT(Scene):
|
||||
|
||||
def __init__(self, robot):
|
||||
super().__init__(robot)
|
||||
# 在这里加入场景中发生的事件
|
||||
# self.event_list = [
|
||||
# # (3, self.set_goal("On(VacuumCup,Bar)"))
|
||||
# (3, self.set_goal("On(Yogurt,Bar)"))
|
||||
# ]
|
||||
self.signal_event_list =[
|
||||
# (3, self.set_goal,("On(Yogurt,Bar)",)),
|
||||
# (3, self.customer_say, ("System", "把盒装冰红茶放到水桌")),
|
||||
(3, self.customer_say, ("System", "打开空调并降低空调温度")),
|
||||
]
|
||||
|
||||
def _reset(self):
|
||||
scene = self.add_walkers([[0, 0], [250, 1200], [-55, 750], [70, -200]])
|
||||
pass
|
||||
|
||||
def _run(self):
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import os
|
||||
from robowaiter.robot.robot import Robot
|
||||
|
||||
robot = Robot()
|
||||
|
||||
# create task
|
||||
task = SceneOT(robot)
|
||||
task.reset()
|
||||
task.run()
|
||||
Loading…
Reference in New Issue